PhD Studentship in Conflict Research with Focus on Forecasting and Machine Learning

Are there recurring patterns in the escalation and emergence of wars? The idea that history may repeat itself is old. But recent advances overcoming methodological and data barriers present an opportunity to identify these recurrences empirically and to examine whether these patterns can be classified to improve forecasts and inform theories of conflict. In this project, we will combine new methods and novel data on conflict from finance, diplomatic cables, and newspapers, to extract typical pre war motifs. Just as DNA sequencing has been critical to medical diagnoses, PaCE aims to diagnose international politics by uncovering the relevant patterns in the area of conflict (more details at

The post-holder will work in an exciting environment alongside a highly creative and motivated team, to acquire and analyse time series data from pre-conflict environments (both interstate and civil war), and apply recent machine learning techniques to forecasting the onset of these events.

Expected start date

September 2022

Standard Duties and Responsibilities

The holder of the position is to pursue a project that uses machine-learning methods to forecast interstate and civil wars. In particular, the team member will:

  • Acquire and process time series data from finance, diplomatic cables, and newspapers
  • Apply Machine Learning methods using Python/R
  • Work on the classification of temporal patterns
  • Contribute to the development of an early warning system for conflict
  • Contribute to the growth of the methodological and substantive expertise of the team
  • Participate in the team’s publications

Person Specification


A strong undergraduate degree or Masters in computer science (engineering, physics also welcome), political science, economics or a relevant field (e.g., first or upper second for Irish or UK applicants; GPA 3.3+ for US applicants).

Knowledge & Experience

  • Excellent knowledge of R and/or Python (highly desirable)
  • Background in statistics and/or econometrics (highly desirable)
  • Knowledge of machine learning (desirable)
  • Familiarity with Social Sciences (desirable)
  • Familiarity with the development of statistical packages and deployment on github (desirable)

Skills & Competencies

  • Ability to write clearly and concisely
  • Enthusiasm to acquire expertise in new fields and methods
  • Enthusiasm to share expertise with other team members
  • Enthusiasm for communication with a broader audience


The studentship will be supervised by Professor Thomas Chadefaux. Students will be enrolled in the Department of Political Science PhD Programme at Trinity College Dublin.


This project is funded by the PaCE ERC Consolidator Grant 101002240 (2022-26) and is hosted at the Department of Political Science at Trinity College Dublin.

Students will receive an annual stipend of €18,300 for four years. Continuation on the PhD register is conditional on the candidate’s successful progression through the PhD requirements. The team member’s stipend will be conditional on satisfactory performance in the team.

The Department of Political Science at Trinity College Dublin

Our group is located in Trinity College, in the heart of Dublin. Trinity was founded in 1592 and is ranked as Ireland’s No.1 University in the QS World University Ranking, THE World University Ranking and the Academic Ranking of World Universities. It is a member of the League of European Research Universities. The Department of Political Science consistently ranks number #1 in Ireland, in the top 20 in Europe and in the top 50 in the world (QS rankings).

Trinity is an equal opportunities employer and is committed to employment policies, procedures and practices which do not discriminate on grounds such as gender, civil status, family status, age, disability, race, religious belief, sexual orientation or membership of the travelling community. On that basis we encourage and welcome talented people from all backgrounds to join our staff community. Trinity’s Diversity Statement can be viewed in full at

Application Procedure

As a first step, candidates should submit to with the subject line “[PaCE] application”:

  • a cover letter addressing why this project interests you and what you will bring to it. Be sure to outline your experience with statistics and computational methods.
  • a full curriculum vitae
  • links to any supporting material;e.g. thesis, code repositories, preprints, articles in the media, etc.

If requested, the candidate will then be asked to submit a formal application to the PhD Programme.

Applications submitted by March 31, 2022 will receive full consideration. The position will remain open until filled.

If you are a Chinese student and would like to apply to the scholarships from the China Scholarship Council please contact Thomas before Jan 31, 2022.

For any query, please contact: Professor Thomas Chadefaux